Fuzzy Petri nets (FPNs) have been widely used for knowledge representation and reasoning in various areas. However, the traditional FPNs have limited abilities in representing uncertain knowledge and conducting approximate reasoning when applied in practical situations. In addition, the knowledge parameters in existing FPNs are usually given by a small number of experts. To address these issues, a grey reasoning Petri net (GRPN) model is proposed in this paper for knowledge representation and reasoning under large group environment. In this model, grey production rules in an expert system are modeled by GRPNs, where grey numbers are used to represent the truth degrees of places, the certainty values and thresholds on output arcs of transitions. The grey weighted Bonferroni mean (GWBM) operator is adopted as a substitute of the classical min and max operators in the developed grey reasoning algorithm to capture the interrelationships of input places and the interrelationships between transitions. Furthermore, a large group decision making method is introduced for obtaining the knowledge parameters of GRPNs based on grey correlation analysis. Finally, the usefulness and effectiveness of the proposed GRPN model is demonstrated by a real-world risk evaluation example.

Objective: To investigate changes in interhemispheric imbalance of cortical excitability during motor recovery after stroke and to clarify the relationship between motor function recovery and alterations in interhemispheric imbalance, with the aim to establish more effective neuromodulation strategies.Methods: Thirty-one patients underwent assessments of resting motor threshold (RMT) using transcranial magnetic stimulation (TMS); the cortical activity of the primary motor cortex (M1), premotor cortex (PMC), and supplementary motor area (SMA) using functional near-infrared spectroscopy (fNIRS); as well as motor function using upper extremity Fugl-Meyer (FMA-UE). The laterality index (LI) of RMT and fNIRS were also calculated. All indicators were measured at baseline(T₁) and 1 month later(T₂). Correlations between motor function outcome and TMS and fNIRS metrics at baseline were analyzed using bivariate correlation.Results: All the motor function (FMA-UE₁, FMA-UE₂, FMA-d₂) and LI-RMT (LI-RMT₁ and LI-RMT₂) had a moderate negative correlation. The higher the corticospinal excitability of the affected hemisphere, the better the motor outcome of the upper extremity, especially in the distal upper extremity (r = −0.366, p = 0.043; r = −0.393, p = 0.029). The greater the activation of the SMA of the unaffected hemisphere, the better the motor outcome, especially in the distal upper extremity (r = −0.356, p = 0.049; r = −0.367, p = 0.042). There was a significant moderate positive correlation observed between LI-RMT₂ and LI-SMA₁ (r = 0.422, p = 0.018). The improvement in motor function was most significant when both LI-RMT₁ and LI-SMA₁ were lower. Besides, in patients dominated by unaffected hemisphere corticospinal excitability during motor recovery, LI-(M1 + SMA + PMC)₂ exhibited a significant moderate positive association with the proximal upper extremity function 1 month later (r = 0.642, p = 0.007).Conclusion: The combination of both TMS and fNIRS can infer the prognosis of motor function to some extent. Which can infer the role of both hemispheres in recovery and may contribute to the development of effective individualized neuromodulation strategies.

Many government agencies and business organizations have realized that it is necessary to consider not only the economic cost but also the road transport emissions when they determine the transport policies and operations. In this study, a patient transportation problem with the aim of reducing transport emissions has been formulated by implementing CVRP model. In order to determine the routes of patient transportation with optimized emissions for targeted hospital, an improved Cuckoo Search (ICS) algorithm is proposed. In this study, a ‘split’ procedure has been implemented to simplify the individual’s representation. A new category of cuckoos has been introduced to improve the ICS’s search ability. Two heuristics have been applied to improve the quality of initial population. A local search mechanism has been embedded in the search procedure to improve the quality of solutions obtained at the end of each iteration. The computational results were encouraging and demonstrated the effectiveness of the proposed solution method.

Corporate social responsibility (CSR) is the concrete practice of sustainable development at the enterprise level, emphasizing the human value in the production process. The proposal of Industry 5.0 begins to take the coordinated development between human and various production factors as one of the key points of corporate sustainable management. Therefore, this paper studies the optimization of medical enterprise’s operations management considering enterprise CSR under Industry 5.0. A mixed-integer programming (MIP) model is developed to maximize the CSR with the consideration of the impact of precision medical technologies such as surgical robots (SRs) and 3D bone printing on employee social welfare, corporate profits, social environment, and customer surplus value. An improved variable neighborhood tabu search (IVNTS) algorithm which combines the variable neighborhood tabu search (VNTS) algorithm and simulated annealing (SA) algorithm is designed to solve the model, and numerical experiments are analyzed to verify the effectiveness of the proposed IVNTS. The research aids medical enterprise to make reasonable operations management decisions, while providing a reference for the government to draft and implement related policies, thereby achieving sustainable social development.

Hereditary spastic paraplegia (HSP) is a heterogeneous group of inherited neurodegenerative disorders that currently have no cure. HSP type 11 (SPG11-HSP) is a complex form carrying mutations in the SPG11 gene. Neuropathological studies demonstrate that motor deficits in these patients are mainly attributed to axonal degeneration of the corticospinal tract (CST). Repetitive transcranial magnetic stimulation (rTMS) is a non-invasive technique that can induce central nervous system plasticity and promote neurological recovery by modulating the excitability of cortical neuronal cells. Although rTMS is expected to be a therapeutic tool for neurodegenerative diseases, no previous studies have applied rTMS to treat motor symptoms in SPG11-HSP. Here, we report a case of SPG11-HSP with lower extremity spasticity and gait instability, which were improved by applying high-frequency rTMS (HF-rTMS) at the primary motor cortex (M1). Clinical and physiological features were measured throughout the treatment, including the Modified Ashworth Scale (MAS), Berg Balance Scale (BBS), the timed up and go (TUG) test and the 10-meter walk test time (10 MWT). The structure and excitability of the CST were assessed by diffusion tensor imaging (DTI) and transcranial magnetic stimulation (TMS), respectively. After treatment, the patient gained 17 points of BBS, along with a gradual decrease in MAS scores of the bilateral lower extremity. In addition, the TUG test and 10 MWT improved to varying degrees. TMS assessment showed increased motor evoked potential (MEP) amplitude, decreased resting motor threshold (RMT), decreased central motor conduction time (CMCT), and decreased difference in the cortical silent period (CSP) between bilateral hemispheres. Using the DTI technique, we observed increased fractional anisotropy (FA) values and decreased mean diffusivity (MD) and radial diffusivity (RD) values in the CST. It suggests that applying HF-rTMS over the bilateral leg area of M1 (M1-LEG) is beneficial for SPG11-HSP. In this study, we demonstrate the potential of rTMS to promote neurological recovery from both functional and structural perspectives. It may provide a clinical rationale for using rTMS in the rehabilitation of HSP patients.